AIP-C01 practice question: Turning responsible AI policy into controls
AI Safety, Security, and Governance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
An energy utility's employee benefits team is launching an assistant that answers staff questions about leave, pay and health plans. The utility has a written responsible AI policy that sets the rules the assistant must follow.
The policy says the assistant must not give medical advice, must not reveal employee identifiers, and must give leave and pay answers that match the approved leave policy document. The leave rules include numeric limits and waiting periods that the assistant must not misstate.
The team wants each rule enforced in the runtime configuration, and it wants a record that ties each control back to the policy clause it implements.
Which approach meets these requirements?
Choose one answer.
AA. Paste the full policy into the system prompt, ask the model to follow it, and review a random sample of conversations each quarter to confirm that the assistant complied with each clause.
BB. Map each policy clause to a guardrail control: a denied topic for medical advice, a sensitive information filter for identifiers, and Automated Reasoning checks for leave answers.
CC. Fine-tune the model on the policy document and on sample sets of approved answers, so the model learns the rules internally, and then test the tuned model on a fixed list of staff questions.
DD. Set every content filter to HIGH strength, and require help desk staff to review each assistant answer about leave or pay before it is sent to an employee.
Show the answer and explanation
Answer: B
A. Wrong: A system prompt is a request the model may not honor, and a quarterly sample cannot show that each clause was followed in every conversation. The option is not enforced at runtime and leaves no clause-level record.
B. Right: Each clause becomes a runtime control in the guardrail, so the rule is enforced on every reply. The Automated Reasoning policy checks numeric leave and pay answers against formal rules from the approved document, and the mapping gives the clause-level record that the team needs. Learn more: Automated Reasoning checks in Amazon Bedrock Guardrails (opens in a new tab)
C. Wrong: Fine-tuning can shape behavior, but it does not enforce each rule on each reply, and a policy change means another training run. A fixed question list also gives no clause-level record of how each rule is enforced. Learn more: Customize your model (opens in a new tab)
Mapping each policy clause to a guardrail control enforces the rule on every reply, and an Automated Reasoning policy checks leave and pay answers against formal rules from the approved document. Keeping that mapping gives the record tying controls to clauses. Prompts, fine-tuning and human review do not enforce each clause at runtime.
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